Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Background/Aims: The incidence of steatotic liver disease (SLD) is increasing across all age groups as the incidence of obesity increases worldwide. The existing noninvasive prediction models for SLD require laboratory tests or imaging and perform poorly in the early diagnosis of infrequently screened populations such as young adults and individuals with healthcare disparities. We developed a machine learning-based point-of-care prediction model for SLD that is readily available to the broader population with the aim of facilitating early detection and timely intervention and ultimately reducing the burden of SLD. Methods: We retrospectively analyzed the clinical data of 28,506 adults who had routine health check-ups in South Korea from January to December 2022. A total of 229,162 individuals were included in the external validation study. Data were analyzed and predictions were made using a logistic regression model with machine learning algorithms. Results: A total of 20,094 individuals were categorized into SLD and non-SLD groups on the basis of the presence of fatty liver disease. We developed three prediction models: SLD model 1, which included age and body mass index (BMI); SLD model 2, which included BMI and body fat per muscle mass; and SLD model 3, which included BMI and visceral fat per muscle mass. In the derivation cohort, the area under the receiver operating characteristic curve (AUROC) was 0.817 for model 1, 0.821 for model 2, and 0.820 for model 3. In the internal validation cohort, 86.9% of individuals were correctly classified by the SLD models. The external validation study revealed an AUROC above 0.84 for all the models. Conclusions As our three novel SLD prediction models are cost-effective, noninvasive, and accessible, they could serve as validated clinical tools for mass screening of SLD.

Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

Objective: The coronavirus disease-2019 (COVID-19) pandemic has significantly altered daily life, potentially impacting children’s digital media usage. This study investigates changes in smart device usage among children in South Korea, considering the pandemic’s effects. Methods: A longitudinal analysis was conducted on a cohort of 313 children aged 8–11 years from 2018 to 2021. The study measured weekly usage of personal computers (PCs), tablet PCs, and smartphones, comparing pre-pandemic (up to 2020) and post-pandemic periods. Partial correlation analysis was employed to assess the impact of COVID-19, controlling for covariates such as age, household income, and parental education. Results: The analysis revealed a significant increase in smart device usage time following the onset of the pandemic. This increase remained statistically significant even after accounting for covariates. Notably, both age and maternal education level were significant factors influencing device usage. Conclusion This study demonstrates a significant shift in the digital behavior of children aged 8–11 in the context of the COVID-19 pandemic. The increase in smart device usage underscores the pandemic’s far-reaching impact on children’s daily routines and suggests a need for further research into its long-term effects. The findings highlight the importance of considering external societal changes when analyzing trends in digital media usage among children.

This study examined the risk of dysphagia and frailty in community-dwelling older adults and identified related factors including general characteristics, depression, daily singing, and healthy lifestyle. Methods: A descriptive correlational study was conducted with 183 older adults aged 65 or older from senior and welfare centers in Korea. Data were collected through self-reported questionnaires between January and February 2025. Measurements included risk of dysphagia, frailty, depression, and healthy lifestyle (smoking, drinking, exercise, and nutrition). Data were analyzed using descriptive statistics, Chi-square test, t-tests, ANOVA, Pearson’s correlation, and multiple regression. Results: The prevalence of pre-frailty was 46.4% (n=85), and the risk of dysphagia was 30.6% (n=56). Dysphagia risk, frailty, depression, daily singing, and healthy lifestyle showed significant correlations. The number of comorbid conditions (β=.26, p<.001), depression (β=.16, p=.037), nutrition (β=-.16, p=.040) were significant predictors of risk of dysphagia with an adjusted R2 of 20.0% (F=8.58, p<.001). For frailty, significant predictors were dysphagia risk (β=.27, p<.001), number of comorbid conditions (β=.23, p<.001), nutrition (β=-.22, p<.001), high school education or above (β=-.15, p=.010), and physical activity (β=-.15, p=.006), with an adjusted R2 of 49.9% (F=13.92, p<.001). Conclusion: The number of comorbid conditions, dysphagia risk, nutritional status, and physical activity were identified as major influencing factors of frailty. Screening for dysphagia risk among older adults with multiple comorbidities, along with interventions to improve nutrition and physical activity, may contribute to the prevention of dysphagia and frailty in this population.